Fully Automated Segmentation of Fiber Bundles in Anatomic Tracing Data

TL;DR

This paper introduces a fully automated, deep learning-based framework for segmenting fiber bundles in anatomic tracer data, significantly improving accuracy and efficiency over existing methods.

Contribution

The authors develop a novel U-Net based approach with large patches and semi-supervised pre-training, enabling accurate, standalone slice analysis of fiber bundles in macaque data.

Findings

Over 20% improvement in sparse bundle detection

40% reduction in false discovery rate

Enables large-scale automated analysis of tracer data

Abstract

Anatomic tracer studies are critical for validating and improving diffusion MRI (dMRI) tractography. However, large-scale analysis of data from such studies is hampered by the labor-intensive process of annotating fiber bundles manually on histological slides. Existing automated methods often miss sparse bundles or require complex post-processing across consecutive sections, limiting their flexibility and generalizability. We present a streamlined, fully automated framework for fiber bundle segmentation in macaque tracer data, based on a U-Net architecture with large patch sizes, foreground aware sampling, and semisupervised pre-training. Our approach eliminates common errors such as mislabeling terminals as bundles, improves detection of sparse bundles by over 20% and reduces the False Discovery Rate (FDR) by 40% compared to the state-of-the-art, all while enabling analysis of standalone slices. This new framework will facilitate the automated analysis of anatomic tracing data at a large scale, generating more ground-truth data that can be used to validate and optimize dMRI tractography methods.